SoroushVahidi/scidocs huggingface dataset:
Identification of extract method refactoring opportunities for the decomposition of methods
(2011)

The extraction of a code fragment into a separate method is one of the most widely performed refactoring activities, since it allows the decomposition of large and complex methods and can be used in combination with other code transformations for fixing a variety of design problems. Despite the significance of Extract Method refactoring towards code quality improvement, there is limited support for the identification of code fragments with distinct functionality that could be extracted into new methods. The goal of our approach is to automatically identify Extract Method refactoring opportunities which are related with the complete computation of a given variable (complete computation slice) and the statements affecting the state of a given object (object state slice). Moreover, a set of rules regarding the preservation of existing odule decomposition dependences is proposed that exclude refactoring opportunities corresponding to slices whose extraction could possibly cause a change in program behavior. The proposed approach has been evaluated regarding its ability to capture slices of code implementing a distinct functionality, its ability to resolve existing design flaws, its impact on the cohesion of the decomposed and extracted methods, and its ability to preserve program behavior. Moreover, precision and recall have been computed employing the refactoring depe opportunities found by in
WHY PEOPLE STAY : USING JOB EMBEDDEDNESS TO PREDICT VOLUNTARY TURNOVER
(2001)

A new construct, entitled job embeddedness, is introduced. Assessing factors from on and off the job, it includes an individual’s (a) links to other people, teams and groups, (b) perception of their fit with their job, organization and community and (c) what they say they would have to sacrifice if they left their job. A measure of job embeddedness is developed with two samples. The results show that job embeddedness predicts the key outcomes of both intent to leave and voluntary turnover, and explains significant incremental variance over and above job satisfaction, organizational commitment, job alternatives and job search. Implications for theory and practice are discussed.
Real-Time Biologically Inspired Action Recognition from Key Poses Using a Neuromorphic Architecture
(2017)

Intelligent agents, such as robots, have to serve a multitude of autonomous functions. Examples are, e.g., collision avoidance, navigation and route planning, active sensing of its environment, or the interaction and non-verbal communication with people in the extended reach space. Here, we focus on the recognition of the action of a human agent based on a biologically inspired visual architecture of analyzing articulated movements. The proposed processing architecture builds upon coarsely segregated streams of sensory processing along different pathways which separately process form and motion information (Layher et al., 2014). Action recognition is performed in an event-based scheme by identifying representations of characteristic pose configurations (key poses) in an image sequence. In line with perceptual studies, key poses are selected unsupervised utilizing a feature-driven criterion which combines extrema in the motion energy with the horizontal and the vertical extendedness of a body shape. Per class representations of key pose frames are learned using a deep convolutional neural network consisting of 15 convolutional layers. The network is trained using the energy-efficient deep neuromorphic networks (Eedn) framework (Esser et al., 2016), which realizes the mapping of the trained synaptic weights onto the IBM Neurosynaptic System platform (Merolla et al., 2014). After the mapping, the trained network achieves real-time capabilities for processing input streams and classify input images at about 1,000 frames per second while the computational stages only consume about 70 mW of energy (without spike transduction). Particularly regarding mobile robotic systems, a low energy profile might be crucial in a variety of application scenarios. Cross-validation results are reported for two different datasets and compared to state-of-the-art action recognition approaches. The results demonstrate, that (I) the presented approach is on par with other key pose based methods described in the literature, which select key pose frames by optimizing classification accuracy, (II) compared to the training on the full set of frames, representations trained on key pose frames result in a higher confidence in class assignments, and (III) key pose representations show promising generalization capabilities in a cross-dataset evaluation.
Hopelessness Depression : A Theory-Based Subtype of Depression
(2004)

We present a revision of the 1978 reformulated theory of helplessness and depression and call it the hopelessness theory of depression. Although the 1978 reformulation has generated a vast amount of empirical work on depression over the past 10 years and recently has been evaluated as a model of depression, we do not think that it presents a clearly articulated theory of depression. We build on the skeletal logic of the 1978 statement and (a) propose a hypothesized subtype of depression— hopelessness depression, (b) introduce hopelessness as a proximal sufficient cause of the symptoms of hopelessness depression, (c) deemphasize causal attributions because inferred negative consequences and inferred negative characteristics about the self are also postulated to contribute to the formation of hopelessness and, in turn, the symptoms of hopelessness depression, and (d) clarify the diathesisstress and causal mediation components implied, but not explicitly articulated, in the 1978 statement. We report promising findings for the hopelessness theory and outline the aspects that still need to be tested.
SSS: a hybrid architecture applied to robot navigation
(1992)

This paper describes a new three layer architecture, SSS, for robot control. It combines a servo-control layer, a Òsubsumption" layer, and a symbolic layer in a way that allows the advantages of each technique to be fully exploited. The key to this synergy is the interface between the individual subsystems. We describe how to build situation recognizers that bridge the gap between the servo and subsumption layers, and event detectors that link the subsumption and symbolic layers. The development of such a combined system is illustrated by a fully implemented indoor navigation example. The resulting real robot, called ÒTJ", is able automatically map office building environments and smoothly navigate through them at the rapid speed of 2.6 feet per second.
Deep Face Recognition: A Survey
(2018)

Face recognition made tremendous leaps in the last five years with a myriad of systems proposing novel techniques substantially backed by deep convolutional neural networks (DCNN). Although face recognition performance sky-rocketed using deep-learning in classic datasets like LFW, leading to the belief that this technique reached human performance, it still remains an open problem in unconstrained environments as demonstrated by the newly released IJB datasets. This survey aims to summarize the main advances in deep face recognition and, more in general, in learning face representations for verification and identification. The survey provides a clear, structured presentation of the principal, state-of-the-art (SOTA) face recognition techniques appearing within the past five years in top computer vision venues. The survey is broken down into multiple parts that follow a standard face recognition pipeline: (a) how SOTA systems are trained and which public data sets have they used; (b) face preprocessing part (detection, alignment, etc.); (c) architecture and loss functions used for transfer learning (d) face recognition for verification and identification. The survey concludes with an overview of the SOTA results at a glance along with some open issues currently overlooked by the community.
Beyond the right hemisphere: brain mechanisms mediating vocal emotional processing
(2006)

Vocal perception is particularly important for understanding a speaker's emotional state and intentions because, unlike facial perception, it is relatively independent of speaker distance and viewing conditions. The idea, derived from brain lesion studies, that vocal emotional comprehension is a special domain of the right hemisphere has failed to receive consistent support from neuroimaging. This conflict can be reconciled if vocal emotional comprehension is viewed as a multi-step process with individual neural representations. This view reveals a processing chain that proceeds from the ventral auditory pathway to brain structures implicated in cognition and emotion. Thus, vocal emotional comprehension appears to be mediated by bilateral mechanisms anchored within sensory, cognitive and emotional processing systems.
Generating English from Abstract Meaning Representations
(2016)

We present a method for generating English sentences from Abstract Meaning Representation (AMR) graphs, exploiting a parallel corpus of AMRs and English sentences. We treat AMR-to-English generation as phrase-based machine translation (PBMT). We introduce a method that learns to linearize tokens of AMR graphs into an English-like order. Our linearization reduces the amount of distortion in PBMT and increases generation quality. We report a Bleu score of 26.8 on the standard AMR/English test set.
A Novel Distributed SDN-Secured Architecture for the IoT
(2016)

Due to their rapid evolution, mobile devices demand for more dynamic and flexible networking services. A major challenges of future mobile networks is the increased mobile traffic. With the recent upcoming technologies of network programmability like Software-Defined Network (SDN), it may be integrated to create a new communication platform for Internet of Things (IoT). In this work, we present how to determine the effectiveness of an approach to build a new secured network architecture based on SDN and clusters. Our proposed scheme is a starting point for some experiments providing perspective over SDN deployment in a cluster environment. With this aim in mind, we suggest a routing protocol that manages routing tasks over Cluster-SDN. By using network virtualization and OpenFlow technologies to generate virtual nodes, we simulate a prototype system controlled by SDN. With our testbed, we are able to manage 500 things. We can analyze every OpenFlow messages and we have discovered that with a particular flow, the things can exchange information unlike the routing principle.
Logistic Regression and Collaborative Filtering for Sponsored Search Term Recommendation
(2006)

Sponsored search advertising is largely based on bidding on individual terms. The richness of natural languages permits web searchers to express their information needs in myriad ways. Advertisers have difficulty discovering all the terms that are relevant to their products or services. We examine the performance of logistic regression and collaborative filtering models on two different data sources to predict terms relevant to a set of seed terms describing an advertiser’s product or service.
Removal of High-Density Salt-and-Pepper Noise in Images With an Iterative Adaptive Fuzzy Filter Using Alpha-Trimmed Mean
(2014)

Suppression of impulse noise in images is an important problem in image processing. In this paper, we propose a novel adaptive iterative fuzzy filter for denoising images corrupted by impulse noise. It operates in two stages-detection of noisy pixels with an adaptive fuzzy detector followed by denoising using a weighted mean filter on the “good” pixels in the filter window. Experimental results demonstrate the algorithm to be superior to state-of-the-art filters. The filter is also shown to be robust to very high levels of noise, retrieving meaningful detail at noise levels as high as 97%.
Ant colony optimization for routing and load-balancing: survey and new directions
(2003)

Although an ant is a simple creature, collectively a colony of ants performs useful tasks such as finding the shortest path to a food source and sharing this information with other ants by depositing pheromone. In the field ofant colony optimization (ACO), models ofcollective intelligenceof ants are transformed into useful optimization techniques that find applications in computer networking. In this survey, the problem-solving paradigm of ACO is explicated and compared to traditional routing algorithms along the issues of routing information, routing overhead and adaptivity. The contributions of this survey include 1) providing a comparison and critique of the state-of-the-art approaches for mitigatingstagnation (a major problem in many ACO algorithms), 2) surveying and comparing three major research in applying ACO in routing and load-balancing, and 3) discussing new directions and identifying open problems. The approaches for mitigating stagnation discussed include:evaporation, aging, pheromone smoothingand limiting, privileged pheromone layingandpheromone-heuristic control . The survey on ACO in routing/load-balancing includes comparison and critique of ant-based control and its ramifications, AntNetand its extensions, as well as ASGAand SynthECA. Discussions on new directions include an ongoing work of the authors in applyingmultiple ant colony optimizationin load-balancing.
It Makes Sense: A Wide-Coverage Word Sense Disambiguation System for Free Text
(2010)

Word sense disambiguation (WSD) systems based on supervised learning achieved the best performance in SensEval and SemEval workshops. However, there are few publicly available open source WSD systems. This limits the use of WSD in other applications, especially for researchers whose research interests are not in WSD. In this paper, we present IMS, a supervised English all-words WSD system. The flexible framework of IMS allows users to integrate different preprocessing tools, additional features, and different classifiers. By default, we use linear support vector machines as the classifier with multiple knowledge-based features. In our implementation, IMS achieves state-of-the-art results on several SensEval and SemEval tasks.
NASARI: a Novel Approach to a Semantically-Aware Representation of Items
(2015)

The semantic representation of individual word senses and concepts is of fundamental importance to several applications in Natural Language Processing. To date, concept modeling techniques have in the main based their representation either on lexicographic resources, such as WordNet, or on encyclopedic resources, such as Wikipedia. We propose a vector representation technique that combines the complementary knowledge of both these types of resource. Thanks to its use of explicit semantics combined with a novel cluster-based dimensionality reduction and an effective weighting scheme, our representation attains state-of-the-art performance on multiple datasets in two standard benchmarks: word similarity and sense clustering. We are releasing our vector representations at http://lcl.uniroma1.it/nasari/.
De-Conflated Semantic Representations
(2016)

One major deficiency of most semantic representation techniques is that they usually model a word type as a single point in the semantic space, hence conflating all the meanings that the word can have. Addressing this issue by learning distinct representations for individual meanings of words has been the subject of several research studies in the past few years. However, the generated sense representations are either not linked to any sense inventory or are unreliable for infrequent word senses. We propose a technique that tackles these problems by de-conflating the representations of words based on the deep knowledge that can be derived from a semantic network. Our approach provides multiple advantages in comparison to the previous approaches, including its high coverage and the ability to generate accurate representations even for infrequent word senses. We carry out evaluations on six datasets across two semantic similarity tasks and report state-of-the-art results on most of them.
Neural Sequence Learning Models for Word Sense Disambiguation
(2017)

Word Sense Disambiguation models exist in many flavors. Even though supervised ones tend to perform best in terms of accuracy, they often lose ground to more flexible knowledge-based solutions, which do not require training by a word expert for every disambiguation target. To bridge this gap we adopt a different perspective and rely on sequence learning to frame the disambiguation problem: we propose and study in depth a series of end-to-end neural architectures directly tailored to the task, from bidirectional Long Short-Term Memory to encoder-decoder models. Our extensive evaluation over standard benchmarks and in multiple languages shows that sequence learning enables more versatile all-words models that consistently lead to state-of-the-art results, even against word experts with engineered features.
Your Exploit is Mine: Automatic Shellcode Transplant for Remote Exploits
(2017)

Developing a remote exploit is not easy. It requires a comprehensive understanding of a vulnerability and delicate techniques to bypass defense mechanisms. As a result, attackers may prefer to reuse an existing exploit and make necessary changes over developing a new exploit from scratch. One such adaptation is the replacement of the original shellcode (i.e., the attacker-injected code that is executed as the final step of the exploit) in the original exploit with a replacement shellcode, resulting in a modified exploit that carries out the actions desired by the attacker as opposed to the original exploit author. We call this a shellcode transplant. Current automated shellcode placement methods are insufficient because they over-constrain the replacement shellcode, and so cannot be used to achieve shellcode transplant. For example, these systems consider the shellcode as an integrated memory chunk and require that the execution path of the modified exploit must be same as the original one. To resolve these issues, we present ShellSwap, a system that uses symbolic tracing, with a combination of shellcode layout remediation and path kneading to achieve shellcode transplant. We evaluated the ShellSwap system on a combination of 20 exploits and 5 pieces of shellcode that are independently developed and different from the original exploit. Among the 100 test cases, our system successfully generated 88% of the exploits.
Manufacturing Problem Solving in a Job Shop — Research in Layout Planning
(2014)

For ensuring efficient operation of a job shop, it is important to minimize waste, which has no value addition to the final product. For a job shop, minimizing movement is considered as the highest priority for waste prevention. For this reason, the layout for a job shop should be designed in such a way to ensure the lowest possible cost for production by reducing nonvalue added activities, such as movement of work-in-process. An effective and efficient way of layout planning for a job shop is a key for solving movement inefficiencies and facilitating communication and interaction between workers and supervisors. This involves relocation of equipment and machinery to streamline materials flow. The primary objective of relocation is to avoid flow conflicts, reduce process time, and increase efficiency of labor usage. Proximity of the most frequently used machines minimizes the movement cost significantly, which eventually minimizes the cost of production. This paper describes the research done in process flow improvements in a job shop manufacturing steel components. The literature focused mainly on mathematical modeling with assumptions that are not applicable for a typical small-scale job shop operation. However, this was overcome by collecting material movement data over three months and analyzing the information using a From-To chart. By analyzing the chart, the actual loads between departments for the operation period were tabulated in available plant space. From this information, the inter-departmental flow was shown by a model. This provides the basic layout pattern, which was improved. A second step was to determine the cost of this layout by multiplying the material handling cost by the number of loads moved between each pair of departments. As a recommendation for solving the problem, two layout models have been developed for ensuring the lowest movement cost. Introduction Transportation is considered as one of the seven wastes for lean manufacturing, and effective layout planning is considered as a key to overcome this kind of waste. It is stated, “Double handling and excessive movements are likely to cause damage and deterioration with the distance of communication between processes” [1]. Therefore, layout planning has clear impact with the quality and quantity of the final products by reducing waste and improving efficiency.
High-power GaN MMIC PA Over 40–4000MHz
(2013)

We report a high-performance GaN MMIC power amplifier operating from 40MHz to 4,000MHz. The MMIC achieved 80W pulsed (100us pulse width and 10% duty cycle) output power (P5dB) with 54% efficiency at 40MHz, 50W with about 30% efficiency across most of the mid band, and gradually decreases to 30W with 22% efficiency at 4000MHz. Power gain is 25dB across the 40-4000MHz band. This ultra wideband performance is achieved by both tailoring the device output impedance, and using a unique wide-band, circuit-matching topology. Detailed design techniques of both the device and the matching circuit will be presented.
Team assembly mechanisms determine collaboration network structure and team performance.
(2005)

Agents in creative enterprises are embedded in networks that inspire, support, and evaluate their work. Here, we investigate how the mechanisms by which creative teams self-assemble determine the structure of these collaboration networks. We propose a model for the self-assembly of creative teams that has its basis in three parameters: team size, the fraction of newcomers in new productions, and the tendency of incumbents to repeat previous collaborations. The model suggests that the emergence of a large connected community of practitioners can be described as a phase transition. We find that team assembly mechanisms determine both the structure of the collaboration network and team performance for teams derived from both artistic and scientific fields.
Internet of Things Business Models
(2015)

Almost all businesses are aware of the potential gains that the Internet of Things (IoT) has to offer, they are unsure of how to approach it. This article proposes a business model that builds on Holler et al., (2014) [1]. The model consists of three dimensions: “Who, Where, and Why”. “Who” describes collaborating partners, which builds the “Value Network”. “Where” describes sources of value co-creation rooted in the layer model of digitized objects, and “Why” describes how partners benefit from collaborating within the value network. With the intention of addressing “How”, the proposed framework has integrated the IoT strategy category, tactics, and value chain elements. The framework is then validated through the case studies of some successful players who are either the Awardees of the IoT Award 2014 or the ICT Award 2015 of Hong Kong.